Overview

Dataset statistics

Number of variables39
Number of observations13354
Missing cells2671
Missing cells (%)0.5%
Duplicate rows224
Duplicate rows (%)1.7%
Total size in memory2.1 MiB
Average record size in memory168.0 B

Variable types

Categorical32
Numeric7

Alerts

year has constant value ""Constant
Dataset has 224 (1.7%) duplicate rowsDuplicates
Price is highly overall correlated with Duration_minutes and 2 other fieldsHigh correlation
Duration_minutes is highly overall correlated with Price and 4 other fieldsHigh correlation
Total_Stops is highly overall correlated with Duration_minutes and 3 other fieldsHigh correlation
month is highly overall correlated with Destination_5High correlation
Airline_4 is highly overall correlated with Additional_Info_5High correlation
Airline_5 is highly overall correlated with Price and 1 other fieldsHigh correlation
Airline_8 is highly overall correlated with Additional_Info_7High correlation
Source_1 is highly overall correlated with Destination_4High correlation
Source_2 is highly overall correlated with Duration_minutes and 3 other fieldsHigh correlation
Source_3 is highly overall correlated with Source_2 and 1 other fieldsHigh correlation
Source_4 is highly overall correlated with Destination_3High correlation
Destination_1 is highly overall correlated with Duration_minutes and 3 other fieldsHigh correlation
Destination_2 is highly overall correlated with Duration_minutes and 1 other fieldsHigh correlation
Destination_3 is highly overall correlated with Source_4High correlation
Destination_4 is highly overall correlated with Source_1High correlation
Destination_5 is highly overall correlated with monthHigh correlation
Additional_Info_3 is highly overall correlated with Price and 1 other fieldsHigh correlation
Additional_Info_5 is highly overall correlated with Airline_4 and 1 other fieldsHigh correlation
Additional_Info_7 is highly overall correlated with Airline_8High correlation
Additional_Info_8 is highly overall correlated with Additional_Info_5High correlation
Airline_2 is highly imbalanced (87.0%)Imbalance
Airline_5 is highly imbalanced (99.3%)Imbalance
Airline_7 is highly imbalanced (98.7%)Imbalance
Airline_8 is highly imbalanced (60.9%)Imbalance
Airline_9 is highly imbalanced (99.9%)Imbalance
Airline_10 is highly imbalanced (73.3%)Imbalance
Airline_11 is highly imbalanced (99.5%)Imbalance
Source_1 is highly imbalanced (78.5%)Imbalance
Source_4 is highly imbalanced (64.9%)Imbalance
Destination_3 is highly imbalanced (64.9%)Imbalance
Destination_4 is highly imbalanced (78.5%)Imbalance
Destination_5 is highly imbalanced (57.2%)Imbalance
Additional_Info_1 is highly imbalanced (99.9%)Imbalance
Additional_Info_2 is highly imbalanced (99.9%)Imbalance
Additional_Info_3 is highly imbalanced (99.5%)Imbalance
Additional_Info_4 is highly imbalanced (99.3%)Imbalance
Additional_Info_6 is highly imbalanced (99.7%)Imbalance
Additional_Info_7 is highly imbalanced (80.7%)Imbalance
Additional_Info_9 is highly imbalanced (99.9%)Imbalance
Price has 2671 (20.0%) missing valuesMissing
Arrival_hour has 411 (3.1%) zerosZeros
Arrival_minutes has 1828 (13.7%) zerosZeros
Dep_minutes has 2590 (19.4%) zerosZeros

Reproduction

Analysis started2023-09-30 02:17:15.845985
Analysis finished2023-09-30 02:17:40.235339
Duration24.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Total_Stops
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
1
7057 
0
4340 
2
1899 
3
 
56
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7057
52.8%
0 4340
32.5%
2 1899
 
14.2%
3 56
 
0.4%
4 2
 
< 0.1%

Length

2023-09-30T07:47:40.426214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:40.633087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7057
52.8%
0 4340
32.5%
2 1899
 
14.2%
3 56
 
0.4%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 7057
52.8%
0 4340
32.5%
2 1899
 
14.2%
3 56
 
0.4%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7057
52.8%
0 4340
32.5%
2 1899
 
14.2%
3 56
 
0.4%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7057
52.8%
0 4340
32.5%
2 1899
 
14.2%
3 56
 
0.4%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7057
52.8%
0 4340
32.5%
2 1899
 
14.2%
3 56
 
0.4%
4 2
 
< 0.1%

Price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1870
Distinct (%)17.5%
Missing2671
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean9087.0641
Minimum1759
Maximum79512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.5 KiB
2023-09-30T07:47:40.848894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1759
5-th percentile3543
Q15277
median8372
Q312373
95-th percentile15764
Maximum79512
Range77753
Interquartile range (IQR)7096

Descriptive statistics

Standard deviation4611.3592
Coefficient of variation (CV)0.50746414
Kurtosis13.30333
Mean9087.0641
Median Absolute Deviation (MAD)3382
Skewness1.8125524
Sum97077106
Variance21264633
MonotonicityNot monotonic
2023-09-30T07:47:41.078748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10262 258
 
1.9%
10844 212
 
1.6%
7229 162
 
1.2%
4804 160
 
1.2%
4823 131
 
1.0%
14714 109
 
0.8%
3943 104
 
0.8%
15129 93
 
0.7%
3841 91
 
0.7%
12898 86
 
0.6%
Other values (1860) 9277
69.5%
(Missing) 2671
 
20.0%
ValueCountFrequency (%)
1759 4
 
< 0.1%
1840 1
 
< 0.1%
1965 36
0.3%
2017 35
0.3%
2050 10
 
0.1%
2071 6
 
< 0.1%
2175 7
 
0.1%
2227 40
0.3%
2228 9
 
0.1%
2385 6
 
< 0.1%
ValueCountFrequency (%)
79512 1
 
< 0.1%
62427 1
 
< 0.1%
57209 1
 
< 0.1%
54826 3
< 0.1%
52285 1
 
< 0.1%
52229 1
 
< 0.1%
46490 1
 
< 0.1%
36983 1
 
< 0.1%
36235 2
< 0.1%
35185 1
 
< 0.1%

date
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.389846
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-09-30T07:47:41.514317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median12
Q321
95-th percentile27
Maximum27
Range26
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.4390603
Coefficient of variation (CV)0.63025822
Kurtosis-1.2594495
Mean13.389846
Median Absolute Deviation (MAD)6
Skewness0.1351419
Sum178808
Variance71.217739
MonotonicityNot monotonic
2023-09-30T07:47:41.668001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 1769
13.2%
6 1627
12.2%
21 1368
10.2%
27 1350
10.1%
1 1349
10.1%
24 1307
9.8%
15 1251
9.4%
12 1214
9.1%
3 1083
8.1%
18 1036
7.8%
ValueCountFrequency (%)
1 1349
10.1%
3 1083
8.1%
6 1627
12.2%
9 1769
13.2%
12 1214
9.1%
15 1251
9.4%
18 1036
7.8%
21 1368
10.2%
24 1307
9.8%
27 1350
10.1%
ValueCountFrequency (%)
27 1350
10.1%
24 1307
9.8%
21 1368
10.2%
18 1036
7.8%
15 1251
9.4%
12 1214
9.1%
9 1769
13.2%
6 1627
12.2%
3 1083
8.1%
1 1349
10.1%

month
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
5
4329 
6
4286 
3
3412 
4
1327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row6
4th row5
5th row3

Common Values

ValueCountFrequency (%)
5 4329
32.4%
6 4286
32.1%
3 3412
25.6%
4 1327
 
9.9%

Length

2023-09-30T07:47:41.836896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:42.023780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5 4329
32.4%
6 4286
32.1%
3 3412
25.6%
4 1327
 
9.9%

Most occurring characters

ValueCountFrequency (%)
5 4329
32.4%
6 4286
32.1%
3 3412
25.6%
4 1327
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4329
32.4%
6 4286
32.1%
3 3412
25.6%
4 1327
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4329
32.4%
6 4286
32.1%
3 3412
25.6%
4 1327
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4329
32.4%
6 4286
32.1%
3 3412
25.6%
4 1327
 
9.9%

year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.6 KiB
2019
13354 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters53416
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 13354
100.0%

Length

2023-09-30T07:47:42.193426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:42.358502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 13354
100.0%

Most occurring characters

ValueCountFrequency (%)
2 13354
25.0%
0 13354
25.0%
1 13354
25.0%
9 13354
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53416
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13354
25.0%
0 13354
25.0%
1 13354
25.0%
9 13354
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13354
25.0%
0 13354
25.0%
1 13354
25.0%
9 13354
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13354
25.0%
0 13354
25.0%
1 13354
25.0%
9 13354
25.0%

Arrival_hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.396061
Minimum0
Maximum23
Zeros411
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-09-30T07:47:42.494514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median14
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.8961454
Coefficient of variation (CV)0.51478904
Kurtosis-1.0772299
Mean13.396061
Median Absolute Deviation (MAD)5
Skewness-0.38459118
Sum178891
Variance47.556821
MonotonicityNot monotonic
2023-09-30T07:47:42.663215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19 2057
15.4%
12 1095
 
8.2%
4 1013
 
7.6%
21 898
 
6.7%
22 837
 
6.3%
1 688
 
5.2%
18 640
 
4.8%
23 608
 
4.6%
8 594
 
4.4%
10 593
 
4.4%
Other values (14) 4331
32.4%
ValueCountFrequency (%)
0 411
3.1%
1 688
5.2%
2 92
 
0.7%
3 61
 
0.5%
4 1013
7.6%
5 95
 
0.7%
6 64
 
0.5%
7 518
3.9%
8 594
4.4%
9 591
4.4%
ValueCountFrequency (%)
23 608
 
4.6%
22 837
6.3%
21 898
6.7%
20 489
 
3.7%
19 2057
15.4%
18 640
 
4.8%
17 242
 
1.8%
16 449
 
3.4%
15 222
 
1.7%
14 360
 
2.7%

Arrival_minutes
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.664146
Minimum0
Maximum55
Zeros1828
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-09-30T07:47:42.823361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q335
95-th percentile50
Maximum55
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.559723
Coefficient of variation (CV)0.67140874
Kurtosis-1.0385669
Mean24.664146
Median Absolute Deviation (MAD)10
Skewness0.11171073
Sum329365
Variance274.22442
MonotonicityNot monotonic
2023-09-30T07:47:42.966824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1828
13.7%
15 1612
12.1%
25 1599
12.0%
35 1365
10.2%
20 1106
8.3%
30 1062
8.0%
50 935
7.0%
45 889
6.7%
5 839
6.3%
40 785
5.9%
Other values (2) 1334
10.0%
ValueCountFrequency (%)
0 1828
13.7%
5 839
6.3%
10 717
 
5.4%
15 1612
12.1%
20 1106
8.3%
25 1599
12.0%
30 1062
8.0%
35 1365
10.2%
40 785
5.9%
45 889
6.7%
ValueCountFrequency (%)
55 617
 
4.6%
50 935
7.0%
45 889
6.7%
40 785
5.9%
35 1365
10.2%
30 1062
8.0%
25 1599
12.0%
20 1106
8.3%
15 1612
12.1%
10 717
5.4%

Dep_hour
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.513254
Minimum0
Maximum23
Zeros51
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-09-30T07:47:43.142340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7362729
Coefficient of variation (CV)0.45841575
Kurtosis-1.1977301
Mean12.513254
Median Absolute Deviation (MAD)5
Skewness0.10908745
Sum167102
Variance32.904827
MonotonicityNot monotonic
2023-09-30T07:47:43.322200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9 1152
 
8.6%
7 1067
 
8.0%
8 872
 
6.5%
6 863
 
6.5%
17 847
 
6.3%
20 826
 
6.2%
5 776
 
5.8%
11 714
 
5.3%
19 710
 
5.3%
10 677
 
5.1%
Other values (14) 4850
36.3%
ValueCountFrequency (%)
0 51
 
0.4%
1 44
 
0.3%
2 228
 
1.7%
3 30
 
0.2%
4 219
 
1.6%
5 776
5.8%
6 863
6.5%
7 1067
8.0%
8 872
6.5%
9 1152
8.6%
ValueCountFrequency (%)
23 189
 
1.4%
22 486
3.6%
21 625
4.7%
20 826
6.2%
19 710
5.3%
18 553
4.1%
17 847
6.3%
16 604
4.5%
15 431
3.2%
14 647
4.8%

Dep_minutes
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.507264
Minimum0
Maximum55
Zeros2590
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-09-30T07:47:43.485245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median25
Q340
95-th percentile55
Maximum55
Range55
Interquartile range (IQR)35

Descriptive statistics

Standard deviation18.832385
Coefficient of variation (CV)0.76844094
Kurtosis-1.3047496
Mean24.507264
Median Absolute Deviation (MAD)20
Skewness0.15939696
Sum327270
Variance354.65871
MonotonicityNot monotonic
2023-09-30T07:47:43.627721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 2590
19.4%
30 1491
11.2%
55 1332
10.0%
45 1106
8.3%
10 1099
8.2%
5 951
 
7.1%
15 876
 
6.6%
25 864
 
6.5%
20 819
 
6.1%
35 813
 
6.1%
Other values (2) 1413
10.6%
ValueCountFrequency (%)
0 2590
19.4%
5 951
 
7.1%
10 1099
8.2%
15 876
 
6.6%
20 819
 
6.1%
25 864
 
6.5%
30 1491
11.2%
35 813
 
6.1%
40 646
 
4.8%
45 1106
8.3%
ValueCountFrequency (%)
55 1332
10.0%
50 767
5.7%
45 1106
8.3%
40 646
4.8%
35 813
6.1%
30 1491
11.2%
25 864
6.5%
20 819
6.1%
15 876
6.6%
10 1099
8.2%

Duration_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct374
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.42287
Minimum5
Maximum2860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.5 KiB
2023-09-30T07:47:43.826945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile90
Q1175
median520
Q3930
95-th percentile1615
Maximum2860
Range2855
Interquartile range (IQR)755

Descriptive statistics

Standard deviation506.71504
Coefficient of variation (CV)0.78875623
Kurtosis-0.14252529
Mean642.42287
Median Absolute Deviation (MAD)350
Skewness0.86788253
Sum8578915
Variance256760.13
MonotonicityNot monotonic
2023-09-30T07:47:44.049632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 672
 
5.0%
90 493
 
3.7%
165 432
 
3.2%
175 418
 
3.1%
155 399
 
3.0%
180 333
 
2.5%
140 286
 
2.1%
150 278
 
2.1%
160 196
 
1.5%
135 164
 
1.2%
Other values (364) 9683
72.5%
ValueCountFrequency (%)
5 2
 
< 0.1%
75 30
 
0.2%
80 81
 
0.6%
85 159
 
1.2%
90 493
3.7%
95 22
 
0.2%
135 164
 
1.2%
140 286
2.1%
145 122
 
0.9%
150 278
2.1%
ValueCountFrequency (%)
2860 1
 
< 0.1%
2820 1
 
< 0.1%
2565 1
 
< 0.1%
2525 1
 
< 0.1%
2480 1
 
< 0.1%
2440 1
 
< 0.1%
2420 1
 
< 0.1%
2345 3
< 0.1%
2315 5
< 0.1%
2300 6
< 0.1%

Airline_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
11162 
1
2192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11162
83.6%
1 2192
 
16.4%

Length

2023-09-30T07:47:44.250030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:44.422787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11162
83.6%
1 2192
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 11162
83.6%
1 2192
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11162
83.6%
1 2192
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11162
83.6%
1 2192
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11162
83.6%
1 2192
 
16.4%

Airline_2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13114 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13114
98.2%
1 240
 
1.8%

Length

2023-09-30T07:47:44.578334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:44.751527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13114
98.2%
1 240
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 13114
98.2%
1 240
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13114
98.2%
1 240
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13114
98.2%
1 240
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13114
98.2%
1 240
 
1.8%

Airline_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
10790 
1
2564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10790
80.8%
1 2564
 
19.2%

Length

2023-09-30T07:47:44.929007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:45.104043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10790
80.8%
1 2564
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 10790
80.8%
1 2564
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10790
80.8%
1 2564
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10790
80.8%
1 2564
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10790
80.8%
1 2564
 
19.2%

Airline_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
8608 
1
4746 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8608
64.5%
1 4746
35.5%

Length

2023-09-30T07:47:45.250936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:45.425851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8608
64.5%
1 4746
35.5%

Most occurring characters

ValueCountFrequency (%)
0 8608
64.5%
1 4746
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8608
64.5%
1 4746
35.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8608
64.5%
1 4746
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8608
64.5%
1 4746
35.5%

Airline_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13346 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Length

2023-09-30T07:47:45.577652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:45.744080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Airline_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
11811 
1
1543 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11811
88.4%
1 1543
 
11.6%

Length

2023-09-30T07:47:45.892097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:46.067377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11811
88.4%
1 1543
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 11811
88.4%
1 1543
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11811
88.4%
1 1543
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11811
88.4%
1 1543
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11811
88.4%
1 1543
 
11.6%

Airline_7
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13338 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13338
99.9%
1 16
 
0.1%

Length

2023-09-30T07:47:46.215806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:46.383987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13338
99.9%
1 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13338
99.9%
1 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13338
99.9%
1 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13338
99.9%
1 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13338
99.9%
1 16
 
0.1%

Airline_8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12328 
1
 
1026

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12328
92.3%
1 1026
 
7.7%

Length

2023-09-30T07:47:46.527896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:46.701788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12328
92.3%
1 1026
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 12328
92.3%
1 1026
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12328
92.3%
1 1026
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12328
92.3%
1 1026
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12328
92.3%
1 1026
 
7.7%

Airline_9
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13353 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Length

2023-09-30T07:47:46.847801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:47.021076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Airline_10
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12746 
1
 
608

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12746
95.4%
1 608
 
4.6%

Length

2023-09-30T07:47:47.165710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:47.334708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12746
95.4%
1 608
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 12746
95.4%
1 608
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12746
95.4%
1 608
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12746
95.4%
1 608
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12746
95.4%
1 608
 
4.6%

Airline_11
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13349 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Length

2023-09-30T07:47:47.483551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:47.659321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Source_1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12898 
1
 
456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Length

2023-09-30T07:47:47.804654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:47.973249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Source_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
7672 
1
5682 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Length

2023-09-30T07:47:48.122677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:48.303564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring characters

ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Source_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
9773 
1
3581 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 9773
73.2%
1 3581
 
26.8%

Length

2023-09-30T07:47:48.456932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:48.629826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9773
73.2%
1 3581
 
26.8%

Most occurring characters

ValueCountFrequency (%)
0 9773
73.2%
1 3581
 
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9773
73.2%
1 3581
 
26.8%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9773
73.2%
1 3581
 
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9773
73.2%
1 3581
 
26.8%

Source_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12471 
1
 
883

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Length

2023-09-30T07:47:48.783847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:48.953173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Destination_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
7672 
1
5682 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Length

2023-09-30T07:47:49.102793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:49.270001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring characters

ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7672
57.5%
1 5682
42.5%

Destination_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
11772 
1
1582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11772
88.2%
1 1582
 
11.8%

Length

2023-09-30T07:47:49.419642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:49.592927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11772
88.2%
1 1582
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 11772
88.2%
1 1582
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11772
88.2%
1 1582
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11772
88.2%
1 1582
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11772
88.2%
1 1582
 
11.8%

Destination_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12471 
1
 
883

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Length

2023-09-30T07:47:49.742518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:49.908421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12471
93.4%
1 883
 
6.6%

Destination_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12898 
1
 
456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Length

2023-09-30T07:47:50.060370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:50.227705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12898
96.6%
1 456
 
3.4%

Destination_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12184 
1
 
1170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12184
91.2%
1 1170
 
8.8%

Length

2023-09-30T07:47:50.370789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:50.541245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12184
91.2%
1 1170
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 12184
91.2%
1 1170
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12184
91.2%
1 1170
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12184
91.2%
1 1170
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12184
91.2%
1 1170
 
8.8%

Additional_Info_1
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13353 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Length

2023-09-30T07:47:50.998956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:51.170002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Additional_Info_2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13353 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Length

2023-09-30T07:47:51.315757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:51.481539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Additional_Info_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13349 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Length

2023-09-30T07:47:51.626265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:51.797348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13349
> 99.9%
1 5
 
< 0.1%

Additional_Info_4
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13346 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Length

2023-09-30T07:47:51.944311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:52.115205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13346
99.9%
1 8
 
0.1%

Additional_Info_5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
10928 
1
2426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10928
81.8%
1 2426
 
18.2%

Length

2023-09-30T07:47:52.258642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:52.428847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10928
81.8%
1 2426
 
18.2%

Most occurring characters

ValueCountFrequency (%)
0 10928
81.8%
1 2426
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10928
81.8%
1 2426
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10928
81.8%
1 2426
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10928
81.8%
1 2426
 
18.2%

Additional_Info_6
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13351 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13351
> 99.9%
1 3
 
< 0.1%

Length

2023-09-30T07:47:52.575577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:52.748846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13351
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13351
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13351
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13351
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13351
> 99.9%
1 3
 
< 0.1%

Additional_Info_7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
12958 
1
 
396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12958
97.0%
1 396
 
3.0%

Length

2023-09-30T07:47:52.889908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:53.065135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12958
97.0%
1 396
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 12958
97.0%
1 396
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12958
97.0%
1 396
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12958
97.0%
1 396
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12958
97.0%
1 396
 
3.0%

Additional_Info_8
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
1
10493 
0
2861 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10493
78.6%
0 2861
 
21.4%

Length

2023-09-30T07:47:53.208240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:53.378541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10493
78.6%
0 2861
 
21.4%

Most occurring characters

ValueCountFrequency (%)
1 10493
78.6%
0 2861
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10493
78.6%
0 2861
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10493
78.6%
0 2861
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10493
78.6%
0 2861
 
21.4%

Additional_Info_9
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size756.5 KiB
0
13353 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13354
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Length

2023-09-30T07:47:53.529746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T07:47:53.701993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13354
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13353
> 99.9%
1 1
 
< 0.1%

Interactions

2023-09-30T07:47:35.746175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:26.056903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:28.039515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:29.844289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:31.220996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:32.622706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:33.981601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:36.353801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:26.498628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:28.340328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:30.066144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:31.442853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:32.835068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:34.216347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:36.641618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:26.845412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:28.544200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:30.251363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:31.637270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:33.022949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:34.408229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:36.907455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:27.149224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:28.738085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:30.437937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:31.820098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:33.205769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:34.596855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:37.230253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:27.364459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:28.954548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:30.629850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:32.036962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:33.390869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:34.791729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:37.525070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:27.567808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:29.235376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:30.817243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:32.223849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:33.584745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:34.978057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:37.810893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:27.782677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:29.641408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:31.008121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:32.416832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:33.779726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-30T07:47:35.180930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-30T07:47:53.902870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
PricedateArrival_hourArrival_minutesDep_hourDep_minutesDuration_minutesTotal_StopsmonthAirline_1Airline_2Airline_3Airline_4Airline_5Airline_6Airline_7Airline_8Airline_9Airline_10Airline_11Source_1Source_2Source_3Source_4Destination_1Destination_2Destination_3Destination_4Destination_5Additional_Info_1Additional_Info_2Additional_Info_3Additional_Info_4Additional_Info_5Additional_Info_6Additional_Info_7Additional_Info_8Additional_Info_9
Price1.000-0.1220.040-0.1040.008-0.0620.6920.3090.1870.0550.0980.3740.4490.8940.1080.0320.2370.0000.0990.0000.1460.2640.1250.1730.2640.3210.1730.1460.2720.1200.1200.8680.0670.1960.0000.1520.1290.000
date-0.1221.000-0.000-0.015-0.003-0.007-0.0240.0650.2010.0250.0030.0470.0790.0370.1070.1000.0390.0000.0400.0070.0180.2440.1980.0480.2440.0450.0480.0180.1210.0000.0000.0330.0460.0570.0200.0380.0430.000
Arrival_hour0.040-0.0001.000-0.1670.0540.0470.0510.1640.1070.2600.0920.2290.3920.0150.3260.0120.1820.0150.1240.0000.1230.3580.1660.1450.3580.1430.1450.1230.1540.0000.0000.0240.0190.2380.0000.1410.2070.010
Arrival_minutes-0.104-0.015-0.1671.0000.063-0.018-0.1130.1780.1270.2800.1730.1920.2900.0310.3340.0070.1350.0120.1310.0340.1420.3550.1950.1010.3550.2510.1010.1420.1140.0000.0000.0200.0380.1740.0000.1040.1690.025
Dep_hour0.008-0.0030.0540.0631.000-0.034-0.0190.1250.0580.1680.0940.1910.1920.0270.2700.0300.1780.0240.1210.0000.0900.1590.2310.2010.1590.1330.2010.0900.0900.0000.0000.0310.0310.1180.0100.1360.1020.005
Dep_minutes-0.062-0.0070.047-0.018-0.0341.000-0.0310.1350.0850.1200.1300.2350.1340.0420.1770.0140.2420.0000.1450.0000.2000.2180.2330.1290.2180.2060.1290.2000.1550.0000.0120.0440.0130.0900.0280.1670.0730.020
Duration_minutes0.692-0.0240.051-0.113-0.019-0.0311.0000.5440.1450.3060.1310.3650.3350.0230.4260.0330.3210.0000.1490.0060.2580.5310.1600.3090.5310.5050.3090.2580.0400.0080.0140.0140.0130.2230.0000.2400.1370.000
Total_Stops0.3090.0650.1640.1780.1250.1350.5441.0000.1340.4000.0620.3000.2660.0000.3160.0280.3090.0000.1230.0220.2700.5230.1310.3200.5230.5280.3200.2700.0690.0120.0120.0060.2520.1970.0000.2350.1120.000
month0.1870.2010.1070.1270.0580.0850.1450.1341.0000.0550.0490.0930.0870.0390.1200.0570.0820.0000.0430.0220.0610.2360.2840.0600.2360.3230.0600.0610.5290.0000.0000.0290.0390.0750.0210.0680.0600.000
Airline_10.0550.0250.2600.2800.1680.1200.3060.4000.0551.0000.0590.2160.3290.0000.1600.0090.1270.0000.0960.0000.0490.0000.0220.0230.0000.0700.0230.0490.0520.0000.0000.0000.0500.2080.0000.0760.2230.000
Airline_20.0980.0030.0920.1730.0940.1300.1310.0620.0490.0591.0000.0650.0990.0000.0470.0000.0370.0000.0270.0000.0220.0070.0400.0340.0070.0960.0340.0220.0190.0000.0000.0000.0000.0620.0000.0200.0690.000
Airline_30.3740.0470.2290.1920.1910.2350.3650.3000.0930.2160.0651.0000.3620.0000.1760.0110.1400.0000.1060.0000.1320.0720.0550.0570.0720.0860.0570.1320.0180.0000.0000.0000.0000.2290.0230.0840.2530.000
Airline_40.4490.0790.3920.2900.1920.1340.3350.2660.0870.3290.0990.3621.0000.0120.2680.0220.2140.0000.1620.0060.1390.0260.1010.0270.0260.0480.0270.1390.0600.0000.0000.0000.0120.5880.0000.1290.4990.000
Airline_50.8940.0370.0150.0310.0270.0420.0230.0000.0390.0000.0000.0000.0121.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0510.0000.0000.5530.0000.0000.0000.0000.0100.000
Airline_60.1080.1070.3260.3340.2700.1770.4260.3160.1200.1600.0470.1760.2680.0001.0000.0030.1030.0000.0780.0000.0670.4200.2180.0950.4200.1320.0950.0670.1110.0000.0000.0000.0000.1010.0000.0620.1240.000
Airline_70.0320.1000.0120.0070.0300.0140.0330.0280.0570.0090.0000.0110.0220.0000.0031.0000.0000.0000.0000.0000.0000.0370.0160.0000.0370.0040.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0130.000
Airline_80.2370.0390.1820.1350.1780.2420.3210.3090.0820.1270.0370.1400.2140.0000.1030.0001.0000.0000.0620.0000.1960.1880.0600.1070.1880.0420.1070.1960.0350.0000.0000.0000.0000.1350.0000.6050.1210.000
Airline_90.0000.0000.0150.0120.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0120.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Airline_100.0990.0400.1240.1310.1210.1450.1490.1230.0430.0960.0270.1060.1620.0000.0780.0000.0620.0001.0000.0000.0540.1450.0510.0130.1450.1150.0130.0540.0170.0000.0000.0000.0000.1020.0000.0360.1130.000
Airline_110.0000.0070.0000.0340.0000.0000.0060.0220.0220.0000.0000.0000.0060.0000.0000.0000.0000.0000.0001.0000.0000.0090.0000.0000.0090.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.000
Source_10.1460.0180.1230.1420.0900.2000.2580.2700.0610.0490.0220.1320.1390.0000.0670.0000.1960.0000.0540.0001.0000.1610.1130.0480.1610.0680.0480.9990.0570.0000.0000.0000.0000.0880.0000.1410.0240.000
Source_20.2640.2440.3580.3550.1590.2180.5310.5230.2360.0000.0070.0720.0260.0000.4200.0370.1880.0000.1450.0090.1611.0000.5210.2291.0000.3150.2290.1610.2660.0000.0000.0090.0160.0230.0000.1350.0370.000
Source_30.1250.1980.1660.1950.2310.2330.1600.1310.2840.0220.0400.0550.1010.0070.2180.0160.0600.0000.0510.0000.1130.5211.0000.1610.5210.2210.1610.1130.1870.0000.0000.0000.0070.0560.0000.0330.0630.000
Source_40.1730.0480.1450.1010.2010.1290.3090.3200.0600.0230.0340.0570.0270.0000.0950.0000.1070.0120.0130.0000.0480.2290.1611.0000.2290.0970.9990.0480.0810.0000.0000.0000.0000.0300.0000.0780.0000.000
Destination_10.2640.2440.3580.3550.1590.2180.5310.5230.2360.0000.0070.0720.0260.0000.4200.0370.1880.0000.1450.0090.1611.0000.5210.2291.0000.3150.2290.1610.2660.0000.0000.0090.0160.0230.0000.1350.0370.000
Destination_20.3210.0450.1430.2510.1330.2060.5050.5280.3230.0700.0960.0860.0480.0000.1320.0040.0420.0000.1150.0000.0680.3150.2210.0970.3151.0000.0970.0680.1130.0000.0000.0000.0000.0390.0000.0450.0190.000
Destination_30.1730.0480.1450.1010.2010.1290.3090.3200.0600.0230.0340.0570.0270.0000.0950.0000.1070.0120.0130.0000.0480.2290.1610.9990.2290.0971.0000.0480.0810.0000.0000.0000.0000.0300.0000.0780.0000.000
Destination_40.1460.0180.1230.1420.0900.2000.2580.2700.0610.0490.0220.1320.1390.0000.0670.0000.1960.0000.0540.0000.9990.1610.1130.0480.1610.0680.0481.0000.0570.0000.0000.0000.0000.0880.0000.1410.0240.000
Destination_50.2720.1210.1540.1140.0900.1550.0400.0690.5290.0520.0190.0180.0600.0510.1110.0000.0350.0000.0170.0270.0570.2660.1870.0810.2660.1130.0810.0571.0000.0090.0090.0550.0730.0000.0390.0300.0000.009
Additional_Info_10.1200.0000.0000.0000.0000.0000.0080.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0091.0000.0000.0000.0000.0000.0000.0000.0000.000
Additional_Info_20.1200.0000.0000.0000.0000.0120.0140.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0001.0000.0000.0000.0000.0000.0000.0000.000
Additional_Info_30.8680.0330.0240.0200.0310.0440.0140.0060.0290.0000.0000.0000.0000.5530.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0090.0000.0000.0000.0550.0000.0001.0000.0000.0000.0000.0000.0310.000
Additional_Info_40.0670.0460.0190.0380.0310.0130.0130.2520.0390.0500.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0160.0070.0000.0160.0000.0000.0000.0730.0000.0000.0001.0000.0000.0000.0000.0420.000
Additional_Info_50.1960.0570.2380.1740.1180.0900.2230.1970.0750.2080.0620.2290.5880.0000.1010.0100.1350.0000.1020.0000.0880.0230.0560.0300.0230.0390.0300.0880.0000.0000.0000.0000.0001.0000.0000.0810.9020.000
Additional_Info_60.0000.0200.0000.0000.0100.0280.0000.0000.0210.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0001.0000.0000.0210.000
Additional_Info_70.1520.0380.1410.1040.1360.1670.2400.2350.0680.0760.0200.0840.1290.0000.0620.0000.6050.0000.0360.0000.1410.1350.0330.0780.1350.0450.0780.1410.0300.0000.0000.0000.0000.0810.0001.0000.3340.000
Additional_Info_80.1290.0430.2070.1690.1020.0730.1370.1120.0600.2230.0690.2530.4990.0100.1240.0130.1210.0000.1130.0000.0240.0370.0630.0000.0370.0190.0000.0240.0000.0000.0000.0310.0420.9020.0210.3341.0000.000
Additional_Info_90.0000.0000.0100.0250.0050.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-09-30T07:47:38.284896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-30T07:47:39.770455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Total_StopsPricedatemonthyearArrival_hourArrival_minutesDep_hourDep_minutesDuration_minutesAirline_1Airline_2Airline_3Airline_4Airline_5Airline_6Airline_7Airline_8Airline_9Airline_10Airline_11Source_1Source_2Source_3Source_4Destination_1Destination_2Destination_3Destination_4Destination_5Additional_Info_1Additional_Info_2Additional_Info_3Additional_Info_4Additional_Info_5Additional_Info_6Additional_Info_7Additional_Info_8Additional_Info_9
003897.02432019110222017000100000000000000001000000010
127662.0152019131555044510000000000001000000000000010
2213882.0962019425925114000010000000010010000000000010
316218.01252019233018532500100000000001000000000000010
4113302.01320192135165028500100000000000000001000000010
503873.0246201911259014500000001000001000000000000010
6111087.012320191025185593000010000000000000001000010000
7122270.01320195580126500010000000000000001000000010
8111087.012320191025855153000010000000000000001000010000
918625.027520191915112547000000100000010010000000000010
Total_StopsPricedatemonthyearArrival_hourArrival_minutesDep_hourDep_minutesDuration_minutesAirline_1Airline_2Airline_3Airline_4Airline_5Airline_6Airline_7Airline_8Airline_9Airline_10Airline_11Source_1Source_2Source_3Source_4Destination_1Destination_2Destination_3Destination_4Destination_5Additional_Info_1Additional_Info_2Additional_Info_3Additional_Info_4Additional_Info_5Additional_Info_6Additional_Info_7Additional_Info_8Additional_Info_9
133442NaN27320194251910199500010000000010010000000000010
133450NaN2152019152513559010000000000000100100000000010
133461NaN1252019745233049501000000000001000000000000010
133471NaN1562019130151561500000100000010010000000000010
133480NaN216201901522459000000001000000100100000000100
133491NaN66201920252030143510000000000001000000000000010
133500NaN27320191655142015500100000000001000000000000010
133511NaN632019425215039500010000000010010000000000010
133521NaN63201919154091510000000000010010000000000010
133531NaN1562019191545586000000100000010010000000000010

Duplicate rows

Most frequently occurring

Total_StopsPricedatemonthyearArrival_hourArrival_minutesDep_hourDep_minutesDuration_minutesAirline_1Airline_2Airline_3Airline_4Airline_5Airline_6Airline_7Airline_8Airline_9Airline_10Airline_11Source_1Source_2Source_3Source_4Destination_1Destination_2Destination_3Destination_4Destination_5Additional_Info_1Additional_Info_2Additional_Info_3Additional_Info_4Additional_Info_5Additional_Info_6Additional_Info_7Additional_Info_8Additional_Info_9# duplicates
3328834.0213201942519101995000100000000100100000000100003
3429181.0246201919152201275100000000000100100000000000103
3729412.027320194255101395000100000000100100000000100003
47210231.0215201919152201275100000000000100100000000000103
53210368.06620194255301375000100000000100100000000100003
73210441.095201919152201275100000000000100100000000000103
91211150.027620191902351195000100000000100100000000100003
92211281.0155201919152201275100000000000100100000000000103
95211552.06320191915752170100000000000100100000000000103
105212392.01852019191517151560100000000000100100000000000103